Group-based treatments for problem alcohol use are designed to reflect the pragmatic realities of treatment settings. One such consideration is that participants are often enrolled into therapy groups on an open, or rolling, basis, as space becomes available in the therapy group. A barrier to testing such interventions has been the interrelatedness of group members' experiences. The ongoing entry and departure of different group participants at different times in open-enrollment groups (OEGs) induces complex correlations among group members' outcomes. Failure to account for this correlation could lead to incorrect statistical tests of treatment effects, undermining our ability to draw conclusions about the effectiveness of treatment. In our previous project, we addressed this correlation by innovatively conceptualizing OEG sessions as spatially related and drawing upon a wealth of existing statistical methodology for spatial data analysis using conditional autoregression (CAR). We demonstrated the versatility of CAR for analyzing data collected during the active treatment phase or post-treatment. Despite these advances, alcohol treatment researchers continue to need new statistical methods to model correlation among OEG participant outcomes. This renewal application responds to NIAAA's recognition of the need to further develop, refine, validate, and creatively implement novel statistical methods for the treatment of alcohol use disorders (PA-13-160). Further guidance is needed regarding how to examine multimodal outcomes that are ubiquitous in group-based alcohol treatment research. Relaxing aspects of the standard CAR approach may result in better-fitting models. Our current methods allow one to estimate a causal treatment effect of a primary outcome for participants who are experimentally assigned to an OEG-based intervention versus a comparison when participants do not interact across study arms. However, a growing body of alcohol treatment research focuses on testing the effects of non- randomized factors on outcomes. Causal inference is challenging when a factor of interest varies among individuals within a therapy group, resulting in interference among individuals.
Specific Aims : (1) Develop innovative approaches to analyze multimodal or semicontinuous outcomes from group therapy studies; (2) explore alternatives to conditional autoregression for flexibly modeling session-to-session correlation; and (3) employ a state-of-the-art causal inference framework that appropriately accounts for the interference among participants in group-based alcohol treatment. Fulfillment of these Specific Aims will provide the alcohol treatment research community innovative statistical methods that synergistically address modeling correlation among OEG participant outcomes while advancing the ability of the field to test the effectiveness of treatment factors of interest.

Public Health Relevance

This proposed research project is relevant to public health because its ultimate goal is to improve group-based treatments of problem alcohol use. Motivated by the ubiquity of group therapy for treating problem alcohol use, this project develops a statistical approach that can be used to test the effectiveness of group-based alcohol treatment in realistic settings.

Agency
National Institute of Health (NIH)
Institute
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Type
Research Project (R01)
Project #
5R01AA019663-05
Application #
9316391
Study Section
National Institute on Alcohol Abuse and Alcoholism Initial Review Group (AA)
Program Officer
Hagman, Brett Thomas
Project Start
2016-07-15
Project End
2019-08-31
Budget Start
2017-09-01
Budget End
2018-08-31
Support Year
5
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Rand Corporation
Department
Type
DUNS #
006914071
City
Santa Monica
State
CA
Country
United States
Zip Code
90401
Burgette, Lane F; Paddock, Susan M (2017) Bayesian models for semicontinuous outcomes in rolling admission therapy groups. Psychol Methods 22:725-742
Paddock, Susan M; Leininger, Thomas J; Hunter, Sarah B (2016) Bayesian restricted spatial regression for examining session features and patient outcomes in open-enrollment group therapy studies. Stat Med 35:97-114
Osilla, Karen Chan; Paddock, Susan M; Leininger, Thomas J et al. (2015) A pilot study comparing in-person and web-based motivational interviewing among adults with a first-time DUI offense. Addict Sci Clin Pract 10:18
Savitsky, Terrance D; Paddock, Susan M (2014) Bayesian Semi- and Non-parametric Models for Longitudinal Data with Multiple Membership Effects in R. J Stat Softw 57:1-35
Paddock, Susan M; Hunter, Sarah B; Leininger, Thomas J (2014) Does group cognitive-behavioral therapy module type moderate depression symptom changes in substance abuse treatment clients? J Subst Abuse Treat 47:78-85
Paddock, Susan M; Savitsky, Terrance D (2013) Discussion of 'Bayesian Nonparametric Inference - Why and How', by Peter Müller and Riten Mitra. Bayesian Anal 8:342-345
Savitsky, Terrance D; Paddock, Susan M (2013) Bayesian Non-Parametric Hierarchical Modeling for Multiple Membership Data in Grouped Attendance Interventions. Ann Appl Stat 7:
Paddock, Susan M; Savitsky, Terrance D (2013) Bayesian Hierarchical Semiparametric Modelling of Longitudinal Post-treatment Outcomes from Open Enrolment Therapy Groups. J R Stat Soc Ser A Stat Soc 176:
Paddock, Susan M; Hunter, Sarah B; Watkins, Katherine E et al. (2011) ANALYSIS OF ROLLING GROUP THERAPY DATA USING CONDITIONALLY AUTOREGRESSIVE PRIORS. Ann Appl Stat 5:605-627